From speeding up documentation and development to rethinking how requirements are built, AI is now part of our day-to-day workflows and real-world projects delivered to clients. And the results? Tangible. Measurable. Exciting.
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These results aren’t projections or assumptions. They’re measured outcomes from real projects, real teams, and real codebases.
80%
less time spent on generating and maintaining public class documentation (measured with Cursor)
50%
faster creation of structured technical documentation for complex systems
30%
less effort to reach up to 100% unit test coverage, depending on the codebase
20%
faster identification and resolution of code violations through integrated tooling
15–20%
time savings for junior developers using AI tools is the boost in velocity we’ve seen in real, controlled experiments across teams
We’ve moved beyond testing. AI is part of how we deliver real value. These results come straight from our projects, where carefully implemented AI tools are making a measurable difference every day.
Reduced time to onboard developers into new domains
especially in regulated industries (thanks to AI knowledge assistants like L.E.A.P.)
Higher quality requirements via AI-supported decomposition
using EARS syntax, reducing ambiguity and review time
Improved cross-team alignment
by capturing and reusing internal know-how via Retrieval-Augmented Generation systems
Fewer regression bugs and faster root-cause analysis
powered by AI-guided traceability and test generation
Documented AI usage patterns and tool performance tracking
ensure continuous feedback and safety in deployment
Safe integrations with our AI implementations
designed to align with ASPICE, ISO 26262, and other safety and security standards
As a QA Lead, I integrated ChatGPT into my daily workflow to support tasks like UML analysis, test case generation, and API test scripting (e.g., Postman, pytest). It significantly accelerated the creation of edge case scenarios and helped convert user stories into Gherkin format for BDD. I also used it to validate JSON structures, prepare test data, and generate technical documentation drafts. The model reduced repetitive manual work and improved consistency across test artifacts. For any QA working in fast-paced environments, it’s a highly effective tool.
Across teams and domains, we test, tweak, and track how AI changes the way we work. Here are real insights from real projects.
Through practical trials and developer feedback, we’ve learned:
With this clarity, we focus on adding real value instead of layering on complexity just for the sake of it.
We ran hands-on experiments to see how AI really impacts developer speed. To make it measurable, we tested tools in real workflows. Here’s what works for us:
We’ve seen up to 20% faster development, especially when building early-stage prototypes or jumping into new tech stacks.
We built and tested our own audit framework to see where AI really fits. It helps us evaluate AI’s impact in real projects, so we’re not guessing, but making informed decisions.
This goes beyond hype-driven adoption. It’s a structured evaluation of where AI provides value, with metrics like delivery speed, code quality, and how ready the team is to adopt new tools.
We don’t silo innovation. Every AI success becomes a shared asset across the organisation. When an insight or workflow works in one domain, we don’t let it stop there: we scale it.
We applied unified AI tooling, adoption standards, and documentation best practices across:
To make AI-assisted development truly useful, we’ve developed internal tools and frameworks that bring clarity, consistency, and measurable impact across teams.
They help us track what’s working, improve what’s not, and keep pushing boundaries.
We’ve built prompt libraries that actually work.
From generating unit tests in embedded C++, to refactoring old Java code, to translating safety requirements into EARS syntax, we have a growing collection of prompts tailored to real engineering challenges.
They’re versioned, domain-specific, and help our teams get faster, more consistent results with AI tools.
We’ve embedded telemetry right into our pipelines to track exactly how AI tools impact story point burn-down, test coverage, and overall team velocity. These insights show us clearly where AI is making a real difference — and where it’s not quite hitting the mark.
Engineering leads gain instant, real-time visibility into the ROI of AI adoption, helping them steer their teams smarter and faster.
We’ve developed handy tools to assess the quality of AI-generated outputs, checking accuracy, coverage, and formatting against what we expect. These utilities can even suggest improved prompts and create alternative versions for easy comparison.
That means prompt engineering stops being a guessing game and turns into a smooth, repeatable process.
Our AI usage dashboards provide engineering and project leads with insights into how and where AI is being used. They show tool adoption rates, output quality trends, developer feedback, and correlations with sprint performance.
Leaders can govern AI usage across distributed teams without micromanaging.
From dev work to documentation and support, we’ve pinpointed the spots where AI actually moves the needle and we’re putting that knowledge to work across real projects.
We integrate tools like GitHub Copilot and Cursor to:
Another powerful way we’re using AI is with LLMs to break down requirements using EARS syntax, boosting:
At Spyrosoft, we put AI to work in customer support – thoughtfully integrated to cut response times, reduce operational costs, and uncover fresh efficiencies.
We built and deployed domain-specific Retrieval-Augmented Generation (RAG) systems for:
• Fast, secure access to internal documentation, specs, and code.
• Onboarding, debugging, reverse engineering, and system design.
• Integration with CI/CD pipelines and developer tools.
This provides engineers with instant, contextual answers from internal sources. No external data exposure, no retraining required.
Qt AI Assistant
Co-created with Qt, this Copilot-like tool for QML/Qt is transforming how developers code.
XGenie
A domain-specific AI co-pilot we developed for the insurance industry to support underwriting.
CodeLlama-13B-QML
We open-sourced a custom fine-tuned LLM with Qt on Hugging Face.
L.E.A.P.
Our internal natural-language assistant that helps engineers instantly access project knowledge.
Have a look at some use cases of AI-assisted development in actual commercial and R&D environments.
• We built a central prompt repository to support AI across common development tasks such as bug fixing, refactoring, test generation, and clarifying specifications.
• This lets engineers reuse polished prompts and get consistent results, which is especially important in regulated environments.
• Plus, prompt performance is tracked, versioned, and fine-tuned over time to keep things sharp.
From developers and testers to product managers and CTOs, we’ve seen how AI speeds things up, improves quality, and makes everyday tasks smoother for every role on the team.
Developers
Code generation and refactoring, AI-assisted onboarding, Copilot prompt libraries, faster documentation, smoother onboarding via domain-specific RAG systems.
Testers
Unit test generation, regression test gap detection, anomaly detection, automated bug reproduction, AI-assisted test coverage analysis.
Product managers / business analysts
Automated requirement decomposition (EARS syntax), faster documentation workflows, knowledge discovery through RAG systems.
Engineering leaders
AI opportunity audits, identifying high-impact integration areas, monitoring AI usage dashboards, scaling AI tools across projects and domains, CI/CD-integrated velocity tracking, measuring AI’s ROI on delivery, workforce enablement, reducing cost-to-deliver across engineering teams.
Support teams
Smart ticket classification and routing, 24/7 technical assistant agents, troubleshooting automation, AI-driven chatbots and voicebots tailored to client needs.
Contact us
AI assisted software development is not a trend. It’s the next chapter of software engineering and it’s happening now.
Whether you’re looking to speed things up, cut costs, or simply get more out of your team, we’re ready to share what we’ve learned and help you make it real.
